Lifestyle-related diseases are one of the greatest threats in modern society. The most widespread diseases include obesity, high blood pressure, diabetes, detrimental cholesterol and blood-fat values, as well as diseases of the cardiovascular system. They are often related to the wrong kind of food, insufficient exercise, insufficient or poor sleep, as well as stress and ultimately burnout. The problems can also often be related to social problems, excessive use of alcohol, and marginalization.
The problem is worldwide and the degree of development of a problem often depends on the degree of development of the society. As the overall welfare of a society increases, problems deriving from it are often encountered. In health-care systems, resources are frequently only sufficient to care for the consequences of problems and not to prevent them. General education relating to lifestyles helps up to a certain point, but individuals often find it difficult to recognise their own problems, never mind solve them.
One way to prevent problems is to monitor a person's physiological signals, on the basis of which conclusions can be drawn on the person's physiological state at the time. The physiological signals from a person's body are often quite difficult to interpret and the interpretation depends on a great deal of other information, such as personal background data (age, height, weight, sex, fitness level, exercise activity), illnesses and their associated medicines, or related context information, such as whether the person is sleeping or awake, at leisure or at work. Together, all of these factors form a highly complex decision surface, which is difficult to manage and the understanding of which demands broad and long training. The interpretation easily becomes dependent on the person involved and subjective, which naturally exposes it to errors and feedback of uneven quality.
In other words, an individual physiological parameter with no context information is very open to interpretation, but if, for example, the same person's heart rate variations while sleeping and during the day or during work and during leisure are known and can be compared, this will already make it considerably easier to determine the person's physiological state. Automatic interpretation of the physiological state combined with measured bio-signals will provide an effective tool for lifestyle counseling.
Earlier methods have typically attempted to standardize the context. This means that a simple test situation is created, in which an attempt has been made to minimize all the other active factors. This, however, loses an important link to real life related to lifestyle counseling, as it is precisely the factors that are standardized to which attention should be paid in lifestyle. Such tests are, for example, investigating the orthostatic heart rate reaction and other tests on the operation of the autonomic nervous system. It has a considerably greater effect to give feedback from an individual's daily life, when real changes in everyday life can also be suggested to the individual. Changes in everyday life are the key to permanent changes in lifestyle. Previous methods aiming at the automation of interpretation are also largely medical programs for creating a diagnosis and cannot be utilized in determining everyday well-being.
In publication U.S. Pat. No. 5,755,671, Albrecht et al. have used frequency bands in heart rate variation when estimating a person's risk of developing cardiovascular diseases. The program compares the heart rate variation frequency bands with predefined values and creates an interpretation on this basis. However, in the method in question only a single variable is used, along with the related reference values, so that it is a very simple method. The interpretation of a single variation without context information does not provide very much information on a person's well-being in normal life.
Sriram et al. in publication WO 2005081168 and Mazar et al. in publication WO 2004047624 have also disclosed a method aimed at automating interpretation which are based on earlier interpretations made by an expert. The computer-aided diagnosis of Sriram et al. is intended to act as support for expert decision making when determining cardiac diseases. The interpretation is based on the patient's data and on an ultrasound image of the heart, so that the analysis is very narrow and is useful only when investigating the state of the patient's heart. For its part, the method of Mazar et al. uses more extensive databases as a base for its interpretation, with the aid of which the patient's health can be determined automatically. The program runs in a web environment and is a so-called remote physician, but is limited to clinical use. In their solution, Mazar et al. do not, however, explain how the person's health is interpreted by combining data from databases and measurement data.
The method disclosed by Hadley in publication WO 2007124271, which concentrates on determining the risk of death due to cardiac failure by monitoring recovery from exercise, also relates to a similar detection of state. The method is connected integrally to a fitness test and to determining heart rate level during loading and recovery. Thus the method cannot be utilized to determine the degree of loading of events in daily life and the analysis it provides is indeed limited very narrowly to the monitoring of heart rate level and on this basis to determining recovery.
In publication U.S. Pat. No. 7,330,752, Kettunen et al. disclose a system for segmenting and analysing an ambulatory heartbeat interval signal, in order to detect a stress state. In the system, it is not possible to interpret or classify whether the detected physiological state is positive or negative in terms of a person's health.
Publication WO 2007/143535 A2, which discloses a device and method for detecting sleep apnea, is also known from the prior art. In the method, three variables are measured and their values are classified as belonging to a specific state depicting sleep quality. However, the classification becomes extremely cumbersome if there is an increase in the number of variables and states depicting sleep quality.